FinanceDataReader VS Deep_Learning_Machine_Learning_Stock

Compare FinanceDataReader vs Deep_Learning_Machine_Learning_Stock and see what are their differences.

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FinanceDataReader Deep_Learning_Machine_Learning_Stock
17 48
950 1,148
- -
0.0 6.1
11 months ago 2 months ago
Jupyter Notebook Jupyter Notebook
GNU General Public License v3.0 only MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

FinanceDataReader

Posts with mentions or reviews of FinanceDataReader. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-06.

What are some alternatives?

When comparing FinanceDataReader and Deep_Learning_Machine_Learning_Stock you can also consider the following projects:

investpy - Financial Data Extraction from Investing.com with Python

tsfresh - Automatic extraction of relevant features from time series:

core-js - Standard Library

bulbea - :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

superstonk - Follow your favourite stock right in the terminal window

TradingGym - Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

FinanceDataReader - Financial data reader [Moved to: https://github.com/financedata-org/FinanceDataReader]

deltapy - DeltaPy - Tabular Data Augmentation (by @firmai)

DataScienceProjects

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

Algo-Trading - This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

Astock - Astock